ssa_view_w_correlation.m

主成分分析和偏最小二乘SquaresPrincipal成分分析（ PCA ）和偏最小二乘（ PLS ）

function ssa_view_w_correlation(SSA);

% last modified 10.02.05

N=length(SSA.singular_numbers);

EV=zeros(N,length(SSA.base_data));
for i=1:N
    EV(i,:)=ssa_get_elementary_vector(SSA.singular_numbers(i),SSA.U(:,i),SSA.V(:,i),SSA.delay)';
end
C=ones(1,length(SSA.base_data)).*N;
for i=1:N
    C(i)=i;
    C(end-i+1)=i;
end
W=zeros(N);
for i=1:N
    for j=1:i
         if i~=j
            a=sum(EV(i,:).*EV(j,:).*C);
            W(i,j)=a;
            W(j,i)=a;
        end
    end
end

tempHandleF=figure('NumberTitle','off','color',[1 1 1],'name','SSA w-correlation');
% x=[]; y=[]; w=[];
for i=1:N
    for j=1:N
        fill([i i i+1 i+1],[j j+1 j+1 j],-W(i,j)), hold on;
% 		x=[x i];
%         y=[y j];
%         w=[w -W(i,j)];
    end
end

% scatter(x,y,ones(size(x))*180,w,'s','filled');
xlim([1 N+1]);
ylim([1 N+1]);

xlabel('elementary vector');
ylabel('elementary vector');
colormap(gray);